Digital signal enhancement by means of algorithmic processing can improve signal quality drastically, in particular when multiple independent sensor readings are available. However, when computational resources are very limited, then the algorithms must be tailored to the task to be as time-efficient as possible. The focus here is the computational enhancement of light measurements from optical sensors as found in today's cameras, CCDs, infrared scanners or laser scanners. The accuracy of these measurements largely depends on the sensor's quality which is subject to production requirements like cost, size, power-consumption etc. However, by means of algorithmic processing of the recorded light signals, it is possible to restore detail and to improve on the signal-to-noise ratio. Detail information is particularly crucial if the ultimate goal is recognition, for example the recognition of human faces in digital images (i.e. the identification of individuals), the recognition of letters or characters, or the recognition of an infrared-scanned or photographed barcode.
Algorithmic processing can enhance signal detail but it requires computing resources which again are subject to practical constraints because of production requirements. This is particularly the case for portable devices, such as camera-equipped mobile phones, handheld barcode scanners or handheld digital cameras where computational resources are very limited.
In the particular example of barcode scanning with a camera, a portable device of today is typically limited in terms of available image resolution. Computational methods known as “super-resolution” methods can be employed to remedy this. However, standard super-resolution methods are computationally too complex for portable devices of today, given that the user expects to scan a barcode within a matter of seconds (or even less than 1 second).
Therefore, there clearly exists a need for a new time-efficient algorithm that is able to recover image detail in real-time thus providing a fluent and pleasant user experience even with today's portable devices which have limited computational resources.